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Enhancing Human Resource Management Practices in Marketing Companies Using Dual Graph Attention Networks
Marketing organization features and strategy implementation have been studied for over 30 years. These include organizational structure, culture, leadership, and processes. HR regulations can motivate marketing professionals to support group and individual goals when correctly implemented, but this part of HR has gotten little attention. Model preparation, feature extraction, and training comprise the suggestive technique. It reviewed data quality, evaluated dataset structure, and described data types during pre-processing. Principal component analysis (PCA) ranked and evaluated decision-making units to reduce dimension. Model training used MGGAN. In comparison to GAN and CNN, the proposed model performed well. With an average accuracy rate of 94.36%, it surpassed earlier approaches and captured all dataset peculiarities. MGGAN modeling can increase predictive performance, and marketing organizations should integrate HR regulations, according to this study. This study opens up new organizational analysis and strategy execution methods. 2025 IEEE. -
ENHANCING home security through visual CRYPTOGRAPHY
Home security systems in the recent times have gained greater importance due to increasing threat in the society. Biometrics deals with automated approaches of recognizing a user or verifying the user identity based on behavioral or physiological features. Visual cryptography is a scheme of secret sharing where a secret image is encrypted into shares which disclose no data independently about the original secret image. As the template of biometric are stored in centralized database due to the threats of security the template of biometric may be changed by attacker. If the template of biometric is changed then the authorized user will not be permitted to access the resource. To manage this problem the schemes of visual cryptography can be used to secure the face recognition. Visual cryptography offers huge ways for supporting such needs of security as well as additional authentication layer. To manage this problem the visual cryptography schemes can be used to secure digital biometric information privacy. In this approach the face or private image is dithered in two varied host images that is sheets and are stored in separate servers of data so as to assure that the original image can get extracted only by accessing both sheets together at a time and a single sheet will not be capable to show any data of private image. The main aim of the study is to propose an algorithm which is a combination of CVC and Siamese network. This research implements visual cryptography for face images in a biometric application. The Siamese network is essential to solve one shot learning by representation of learning feature that are compared to verification tasks. In this research face authentication helps in accomplishing robustness by locating face image from an n input image. This research explores the availability of using visual cryptography for securing the privacy to biometric data. The results of the proposed approach provide an accuracy of 93% which is found to be superior when compared with that of the approaches that are already in practice. 2020 -
Enhancing Heritage and Cultural Education through Immersive Audio-Visual Techniques
Growth in immersive media technologies offers unique opportunities for improved learning experiences. This is important in understanding and preserving heritage and cultural elements. The establishment of hybrid classrooms and the adoption of technologies like virtual reality (VR), augmented reality (AR), and artificial intelligence (AI) make the learning experience more immersive and interactive. These experiences can lessen the differences between theoretical and emotional connections which also help learning to be more impactful and effective. This chapter examines how immersive technologies such as audio-visual technology can change the heritage and cultural education of people across diverse ages. The chapter discussed both 3D soundscapes and visual imagery in relation to VR/AR applications and how well these elements are integrated into a unified whole, which provides some interesting immersive cultural heritage education experiences. This study also analyzes use cases such as virtual museum tours with immersive audio-embedded recreations of historical environments, AR-enhanced archaeological site visits supported by ambient sounds, and interactive cultural heritage simulations guided by spatial audio. It further explores successful research integrating 3D soundscapes into AR/VR use cases. These applications highlight potential VR/AR educational contexts where audio-visual enhancement provides multisensory learning that encourages engagement and cognition surrounding heritage and cultural elements. The study also covers the benefits of this approach like increased public engagement with cultural sites and artifacts, improved retention of historical information, and enhanced accessibility to cultural experiences. Ultra-low latency, higher connectivity, and high-speed data transfer would mean that the quality and accessibility offered by 6G will be on another level altogether. On the other hand, AI can bring immersive experiences along with customization, immediate feedback, and interactions in virtual worlds. The chapter further focuses on the opportunities provided by emerging technologies such as 6G and AI tools in improving understanding of culture/heritage preservation and cultures. The chapter ends with the user applicability of audio-visual enhanced VR/ AR in cultural and heritage education. The study suggests continued research and collaboration between various stakeholders to develop better methods to preserve and disseminate the rich heritage and cultural values. Integrating evolving technologies can be an aid to reducing many of the existing limitations by providing better realistic, interactive, and accessible cultural educational tools. 2026 Scrivener Publishing LLC. All rights reserved. -
ENHANCING HEALTHCARE SECURITY WITH BLOCKCHAIN-POWERED SMART CONTRACTS
The rationale behind this research stems from the increasing frequency of data breaches in healthcare and the inadequacy of centralized systems to ensure privacy, interoperability, and regulatory compliance. The Present study emphasizes the importance of applying security in healthcare. This model was prepared by utilizing Smart Contracts. It has been noted that there are some emerging concerns about data security and privacy as well as interoperability within healthcare organizations. The focus of a research paper is on the deployment of Smart Contracts along with blockchain technologies. The fundamental vision is to improve healthcare infrastructures security. Blockchain is transforming healthcare systems for the better by eliminating inefficiencies caused by fraud and outdated technologies, allowing for the efficient, transparent, and secure issuance of Smart Contracts. The challenges of confidentiality, data security, and access to relevant patient information for medical professionals have been a problem in the healthcare sector. Most of the existing EHR systems do not have adequate mechanisms for enforcing security access controls, which hampers cooperation between healthcare institutions. These security concerns pose risks for patients privacy and cripple the adoption of modern information technology within the health sector. Simulation works shows that Transaction processing time in case of proposed model is below 1.5 second where as it is 2.5 in case of conventional model. Security breach probability of proposed model has been reduced to 0.05 that was 0.35 in case of conventional model. Data integrity verification time in case of proposed model is below 1.0 that is above 1.75 in case of conventional model. While with the existing Electronic Health Record (EHR) systems face limitations in security, privacy enforcement, and interoperability, this study addresses the lack of automated, decentralized access control mechanisms. It proposes a blockchain-powered Smart Contract model to fill these gaps and enhance healthcare data governance and trust. Little Lion Scientific -
Enhancing Healthcare Ecosystems Through the Integration of IoT for Patient-Centric Solutions
The Internet of Things (IoT) is a newly implemented technology in the field of healthcare and can enhance patient-centrical care and efficiency in the healthcare field. IoT may be used to assist in delivering real-time health data and predictive diagnostics and custom care plans by interconnecting medical equipment, sensors, and information systems. This paper will discuss how the IoT technologies, particularly wearable sensors, cloud-based analytics and smart health architectures are changing the way healthcare is delivered. The article highlights how the data merge on the utilization of clinical and non-clinical sources to aid in remote patient monitoring, resources use optimization, and positive patient outcomes. It also identifies the concerns of the implementation of IoT such as the security risks, data privacy and the failure to connect the devices and systems. To address them, the paper discusses the new structures that integrate blockchain and artificial intelligence and ensure safe implementation of data management and heightened clinical decision-making. The results of various works of 2015-2020 have revealed that IoT applications and patent health care-related tendencies are growing, which implies that the shift towards interconnected and intelligent ecosystems is rapid. The consequences of this digital transformation are not confined to the hospital sphere only, as it is extended to homecare, telemedicine, and state population management. Lastly, IoT will allow healthcare stakeholders to shift their healthcare systems to patient-centered, rather than hospital-centered systems, in which a focus on accessibility, efficiency, and personalization would be placed on healthcare provision. 2025 IEEE. -
Enhancing greedy web proxy caching using weighted random indexing based data mining classifier /
Egyptian Informatics Journal, Vol.20, Issue 2, pp. 117-130, ISSN No. 1110-8665. -
Enhancing Greedy Web Proxy caching using Weighted Random Indexing based Data Mining Classifier
Web Proxy caching system is an intermediary between the Web users and servers that try to alleviate the loads on the origin servers by caching particular Web objects and behaves as the proxy for the server and services the requests that are made to the servers. In this paper, the performance of a Proxy system is measured by the number of hits at the Proxy. Higher number of hits at the Proxy server reflects the effectiveness of the Proxy system. The number of hits is determined by the replacement policies chosen by the Proxy systems. Traditional replacement policies that are based on time and size are reactive and do not consider the events that will possibly happen in the future. The performance of the web proxy caching system is improved by adapting Data Mining Classifier model based on Web User clustering and Weighted Random Indexing Methods. The outcome of the paper are proactive strategies that augment the traditional replacement policies such as GDS, GDSF, GD? which uses the Data Mining techniques. 2019 -
Enhancing Glaucoma Detection in Fundus Images: A ResNet based Segmentation and Advanced ML Algorithms with Duck Pack Optimizer
Untreated glaucoma, a chronic eye illness, can cause irreversible vision loss if not caught early. The condition begins with abnormalities in the eye's drainage flow, leading to a rise in intraocular pressure. As the disease progresses, the optic nerve head deteriorates, resulting in vision loss. Ophthalmologists need extensive training and expertise to interpret findings accurately during medical follow-ups to examine the retina. To address this challenge, deep learning-based algorithms have been developed to screen for and diagnose glaucoma using images of the optic nerve, retinal structures, and retinal fundus. This research explores the use of classification and segmentation algorithms based on ResNet to identify glaucoma in fundus images. We fine-tuned the classifier using the DuckPack optimizer and employed XGBoost, LightGBM, and CatBoost algorithms for classification. The results were promising. The segmentation model based on ResNet effectively extracted features, aiding the classification models in accurately identifying glaucoma. All three algorithms performed admirably, though further fine-tuning is needed to determine the best one. Enhancing the model's performance was straightforward after using the DuckPack optimizer for fine-tuning. This study highlights the promising applications of deep learning and sophisticated machine learning algorithms in glaucoma detection. Its findings could inform the development of future diagnostic tools. The Author(s) 2025. -
ENHANCING FOREST ECOSYSTEM RESILIENCE TO CLIMATE CHANGE WITH VANET AND INTEGRATED NATURAL RESOURCES MODELLING
Forest ecosystems are immediately threatened by rising global temperatures and changing climatic patterns. Periodic assessments also contribute to a reduction in the frequency of monitor-ing, which could cause environmental changes to go unnoticed. This work develops a novel real-time monitoring and early warning system to meet this difficulty. By integrating Vehicular Ad Hoc Networks (VANET) with sophisticated natural resources modelling, the proposed method aims to revolutionise the way forest ecosystems are managed. This study strives to design and implement a comprehensive system that harnesses the power of VANET to collect real-time data from sensors deployed on vehicles, and integrates advanced modelling to predict, assess, and mitigate risks to forest ecosystems. The proposed method involves deploying a network of vehicles equipped with environmental sensors within VANET. These sensors continuously collect data on crucial environmental parameters, such as temperature, humidity, air quality, and spatial information. The data are transmitted through a secure VANET communication protocol to a centralised processing unit, where it is integrated with climate models and ecosystem dynamics models. Resilience metrics and thresholds are defined to trigger a tiered early warning system. Preliminary testing of the system demonstrates promising accuracy and responsiveness. The integrated approach allows for dynamic risk assessment, enabling the identification of potential threats such as extreme weather events, invasive species, or disease outbreaks. Early warnings prompt adaptive management strategies, showcasing the systems potential to significantly enhance forest ecosystem resilience. This research presents a pioneering solution to the escalating challenges faced by forest ecosystems in the time of climate change. The real-time monitoring, early warning system, amalgamating VANET and integrated modelling, stand as a robust tool for forest managers, policymakers, and communities to proactively address environmental changes. The findings underscore the systems potential to transform forest management practices, marking a critical step toward sustainable and resilient ecosystems. 2024, Scibulcom Ltd. All rights reserved. -
Enhancing Food E-Commerce Through Immersive Virtual Reality: An Reality: An Extended Technology Acceptance Model Approach for Consumer Adoption in the Post-Pandemic Era
Food purchasing differs from other types of internet shopping. With the introduction of the new retail structure, nearly every e-commerce platform has set up fresh food retail one after another. As a result, electronic gadgets have evolved into tools that marketers may use to initiate interactions with customers. Brands may use augmented reality enabled mobile applications to deliver precise information about products and services while also influencing consumer impressions. Perceived usefulness was the only factor that supported perceived ease of use as a mediator. Our findings provide useful information for researchers and industry experts to improve the effectiveness of VR systems by better understanding user adoption. 2025 IEEE. -
Enhancing food crop classification in agriculture through dipper throat optimization and deep learning with remote sensing
Remote sensing images (RSIs), a keystone of modern agricultural technology, refer to spectral or visual data captured from drones, satellites, or aircraft without direct physical contact with the Earth's surface. These images provide a wide-ranging view of agricultural landscapes, providing valuable insights into land use, crop health, and environmental conditions. Agricultural food crop classification, a vital application within precision agriculture, includes the detection and classification of different crops cultivated in a certain region. Traditionally reliant on manual techniques, the development of technologies, particularly the incorporation of RSIs, has revolutionized this process. Agricultural food crop classification has become more sophisticated and automated by harnessing the wealth of data received from RS, which facilitates precise management and monitoring of crops on a large scale. Deep learning (DL), a branch of artificial intelligence, plays a more effective role in these synergies. The incorporation of DL into the RSI analysis enables high-precision and efficient detection of various crop types, assisting more informed decision-making in agriculture. This study proposes a new Dipper Throat Optimization Algorithm with Deep Learning based Food Crop Classification (DTOADL-FCC) algorithm using Remote Sensing Imaging for Agricultural Resource Management. The DTOADL-FCC method aims to apply DL algorithms for the classification of different crop types. In the DTOADL-FCC method, fully convolutional network (FCN) based segmentation process is performed. Next, the DTOADL-FCC method exploits the SE-ResNet model for learning intrinsic and complex features. The DTOADL-FCC method makes use of DTOA for the hyperparameter tuning process. Lastly, the classification of crop types takes place using the extreme learning machine (ELM) model. The study utilizes mathematical formulations including activation functions, loss functions, fitness calculations, and iterative update processes. A brief set of simulations showcases that the DTOADL-FCC method achieves remarkable performance over other techniques with much improved results. 2024 The Author(s) -
ENHANCING FAKE NEWS DETECTION ON SOCIAL MEDIA THROUGH ADVANCED MACHINE LEARNING AND USER PROFILE ANALYSIS
Social media news consumption is growing in popularity. Users find social media appealing because it's inexpensive, easy to use, and information spreads quickly. Social media does, however, also contribute to the spread of false information. The detection of fake news has gained more attention due to the negative effects it has on society. However, since fake news is created to seem like real news, the detection performance when relying solely on news contents is typically unsatisfactory. Therefore, a thorough understanding of the connection between fake news and social media user profiles is required. In order to detect fake news, this research paper investigates the use of machine learning techniques, covering important topics like feature integration, user profiles, and dataset analysis. To generate extensive feature sets, the study integrates User Profile Features (UPF), Linguistic Inquiry and Word Count (LIWC) features, and Rhetorical Structure Theory (RST) features. Principal Component Analysis (PCA) is used to reduce dimensionality and lessen the difficulties presented by high-dimensional datasets. The study entails a comprehensive assessment of multiple machine learning models using datasets from "Politifact" and "Gossipofact," which cover a range of data processing methods. The evaluation of the XGBoost classification model is further enhanced by the analysis of Receiver Operating Characteristic (ROC) curves. The results demonstrate the effectiveness of particular combinations of features and models, with XGBoost outperforming other models on the suggested unified feature set (ALL). 2023 Little Lion Scientific. -
Enhancing fabric quality with AI-based defect detection systems
In summary, there is a necessity to use AI-based defect detection systems in fabric quality improvement especially in the process of textile production. These sophisticated solutions eliminate the requirement for time-consuming and error-prone traditional manual procedures, and thus not only speed up the inspection but guarantee a higher quality of the products. -
Enhancing Experimental Efficiency in Uncertain Data: A Comparative Analysis of Neutrosophic and Classical Latin Square Designs
This research investigates the relative efficiency between Neutrosophic Latin Square Design (NLSD) and Classical Latin Square Design (CLSD), with a particular focus on their use in situations where data is uncertain and ambiguous. Although CLSD is a classic experiment designed for systematic error control, its utility is limited in fields like agriculture and behavioral sciences due to its performance bottleneck regarding data imprecision. The NLSD can relatively easily be extended to incorporate neutrosophic logic to address these challenges, making it a more powerful tool for modeling uncertainty. In this paper, a systematic efficiency evaluation of NLSD against CLSD is performed for inconsistent data. It is found that the NLSD enables significant improvements in experimental efficiency while providing clearer inferences regarding treatment effects and supporting more reliable conclusions. Despite these limitations, these benefits establish NLSD as a promising candidate for overcoming environmental uncertainties, and these observations hold significant potential to further the advancement of experimental designs. The results demonstrate that NLSD conveys a 55 % chance to enhance efficiency relative to LSD, which is especially important in processes that must attain maximum resource utilization and high experimental efficiency. 2025, Ayandegan Institute of Higher Education. All rights reserved. -
Enhancing Experiences: The Integration of AI in Augmented and Virtual Reality
This chapter examines the integration of Artificial Intelligence (AI) in Augmented Reality (AR) and Virtual Reality (VR), highlighting how AI-driven innovations enhance interactivity, personalization, and real-t ime adaptation in immersive experiences. Through technologies like computer vision, machine learning, and natural language processing, AI enables AR and VR applications to better understand, respond to, and anticipate user needs. Applications of AI-augmented AR and VR are explored across various sectors, including healthcare, education, and industry, where AI-driven systems offer personalized training, virtual assistance, and adaptive simulations. As Mixed Reality (MR) evolves, edge computing plays a key role in improving performance, minimizing latency, and enabling seamless real-time interactions. The chapter also addresses ethical and technical challenges, such as privacy, bias, and processing limitations, emphasizing AIs transformative role in the future of immersive technologies. 2025 by IGI Global Scientific Publishing. All rights reserved. -
ENHANCING EXECUTIVE FUNCTIONS THROUGH COGNITIVE-BASED INTERVENTION IN INDIVIDUALS WITH SUICIDAL IDEATION AND ATTEMPTS: A Mixed-Method Pilot Study
One of the primary causes of death around the world can be attributed to suicidality. Almost 1 million people across the globe commit suicide annually. Neurocognition has an impact on suicidal ideation, and deficits in cognitive markers influence the progression of suicide-related thoughts to behaviours. The present study aims to determine the efficacy of cognitive-based intervention on executive functions implicated in suicidal ideation and suicide attempters. A mixed-method approach was followed, which involved intervention and a quantitative and qualitative analysis. A group of 22 participants aged between 18 and 25 years with suicidal ideation and behaviour was chosen. Ten participants reported having suicidal ideation and no history of suicide attempt or self-harm, whereas 12 participants reported having suicidal ideation and at least one attempt at self-harm or suicidal behaviour. All the participants were assessed on planning, verbal fluency, and response inhibition tests. The participants then receive eight sessions of cognitive-behavioural intervention focusing on suicidal behaviour and thoughts. Post-therapy, the participants underwent a reassessment of their executive functions. The results suggested that cognitive behaviour-based therapy significantly improved planning, verbal fluency, and response inhibition. The feeling of entrapment and the level of depression were qualitatively found to be influencing suicidal ideation and suicide attempts. The study paves the way for further exploration of factors that predict suicide and determines the cause-and-effect relationship between the factors. 2026 selection and editorial matter, K. Jayasankara Reddy; individual chapters, the contributors. All rights reserved. -
Enhancing environmental sound classification with weighted attention-based spectrogram fusion and overlapping pre-patching
Environmental Sound Classification (ESC) remains challenging due to the diverse and overlapping acoustic characteristics of real-world environments. Traditional models relying on single-feature representations such as Mel spectrograms often fail to capture the full range of spectral and temporal details. This paper introduces a novel algorithm Weighted Attention-based Spectrogram Fusion (WASF) that adaptively integrates Mel spectrograms, Cochleograms, and Correlograms using a hierarchical attention mechanism across channel, temporal, and frequency dimensions. Compared to traditional fusion techniques, WASF uses a learnable attention mechanism to dynamically weight each feature's importance over time and frequency, improving the model's capacity to focus on important acoustic cues. In addition, an overlapping pre-patching strategy is proposed to preserve local temporal continuity, enhancing transformer-based modeling. Proposed model demonstrates superior performance with 95.71 % accuracy on UrbanSound8K, 93.97 % on ESC-50, and 94.91 % on ESC-10 datasets. Extensive ablation studies and interpretability analysis validate the effectiveness of each component, demonstrating robustness across diverse acoustic environments and noise conditions. The computational efficiency and interpretable attention patterns make our approach suitable for real-time deployment in smart city applications, surveillance systems, and assistive technologies. 2025 Elsevier B.V. -
Enhancing English Learning Through Digital Storytelling in Indian Schools
This study examines the effectiveness of the Digital Storytelling (DST) teaching approach in improving English learning among ninth graders in four schools in Bengaluru, India. Using a sequential mixed-methods design, the quantitative phase included a non-randomized, post-test-only quasi-experimental design with 200 students divided into a DST-based experimental group and a traditional control group of 100 students each. Quantitative data were collected using a 12-item survey questionnaire, while qualitative data included self-reflection logs from 100 and interviews with 20 students from the experimental group. The results show that DST significantly improves language development and student satisfaction. This is evidenced by higher and more consistent post-test scores in the experimental group, with statistical significance confirmed by the Wilcoxon test. Increased engagement, understanding, and motivation reported by students are consistent with the quantitative improvements. 2025 IGI Global. All rights reserved. -
Enhancing empowerment: Exploring the influence of tourism social entrepreneurship on community engagement
Social entrepreneurship is an evolving force in solving societal challenges, integrating financial viability with social effect. This study delves into the intersection of social entrepreneurship and tourism, exploring how Tourism Social Entrepreneurship fosters community engagement to boost empowerment within locations. Within the tourism sector, Tourism Social Entrepreneurship attempts to stimulate socioeconomic growth while focusing on the welfare of host communities. Despite its potential, a gap persists in knowing how Tourism Social Entrepreneurship efficiently includes and engages local people. This research aims to fill the void by investigating how social entrepreneurs within the tourist sector engage with and empower local communities. By analyzing current literature on social entrepreneurship and conceptualizing Tourism Social Entrepreneurship, this study provides a complete understanding of the dynamics of community engagement in tourism-driven social entrepreneurship endeavors. 2025, IGI Global Scientific Publishing. All rights reserved. -
Enhancing Employee Onboarding Through Digital Twin Technology
Digital twins and their transformative technology have simplified corporate processes while establishing secure and data-driven environments for customized expertise enhancement, workflow optimization, and scenario building. This chapter assesses the impact of digital twins on job design and human resource (HR) practices in developing new and more productive human-focused workplaces. Traditional or old-fashioned onboarding practices are not customizable or flexible and do not give real-time feedback, which creates negative workplace experiences and higher turnover levels. Digital transformation (DT) techniques suggest using digital twins for interactive, customized onboarding solutions to engage and retain employees better. Research indicates that digital twins have effects fundamentally altering HR practices by providing adaptive approaches in real time, cost-free scenario testing, and prediction analytics on employee retention. They help in monitoring workload, preventing burn-out, and customize approaches to enhance individual well-being. Most importantly, they predict career paths and determine training needs; hence, they are good at improving talent development. Successful implementation, however, is dependent on resolving challenges such as data privacy, model accuracy, system interoperability, and bias in artificial intelligence (AI). Applications of digital twin technology (DTT) in onboarding are numerous and important in real life concerning the retention, productivity, and attrition levels of employees. Real-time and predictive data can allow proactive improvement in onboarding and the happiness levels of workers as well as overall company performance results. 2026 Channi Sachdeva, Veena Grover, Balamurugan Balusamy, Veer P. Gangwar, and Pardeep Kumar.